AI Model Quantization: Reducing Memory Usage Without Sacrificing Performance
Optimize AI models for production with quantization on Runpod—reduce memory usage by up to 80% and boost inference speed using 8-bit or 4-bit precision on A100/H100 GPUs, with Dockerized workflows and serverless deployment at scale.
Guides
Edge AI Deployment: Running GPU-Accelerated Models at the Network Edge
Deploy low-latency, privacy-first AI models at the edge using Runpod—prototype and optimize GPU-accelerated inference on RTX and Jetson-class hardware, then scale with Dockerized workflows, secure containers, and serverless endpoints.
Guides
The Complete Guide to Multi-GPU Training: Scaling AI Models Beyond Single-Card Limitations
Train trillion-scale models efficiently with multi-GPU infrastructure on Runpod—use A100/H100 clusters, advanced parallelism strategies (data, model, pipeline), and pay-per-second pricing to accelerate training from months to days.
Guides
Creating High-Quality Videos with CogVideoX on RunPod's GPU Cloud
Generate high-quality 10-second AI videos with CogVideoX on Runpod—leverage L40S GPUs, Dockerized PyTorch workflows, and scalable serverless infrastructure to produce compelling motion-accurate content for marketing, animation, and prototyping.
Guides
Creating Voice AI with Tortoise TTS on RunPod Using Docker Environments
Create human-like speech with Tortoise TTS on Runpod—synthesize emotional, high-fidelity audio using RTX 4090 GPUs, Dockerized environments, and scalable endpoints for real-time voice cloning and accessibility applications.
Guides
Building Real‑Time Recommendation Systems with GPU‑Accelerated Vector Search on Runpod
Build real-time recommendation systems with GPU-accelerated FAISS and RAPIDS cuVS on Runpod—achieve 6–15× faster retrieval using A100/H100 GPUs, serverless APIs, and scalable vector search pipelines with per-second billing.
Guides
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